mindspore.ops.pdist

mindspore.ops.pdist(input, p=2.0)[source]

Calculates the distance between every pair of row vectors in the input using the p-norm. If the input input is a 2D Tensor with shape \((N, M)\), the output must be a 1D Tensor with shape \((N * (N - 1) / 2,)\). If input has batch dimension with shape \((*B, N, M)\), then the output must be a Tensor with shape \((*B, N * (N - 1) / 2)\).

\[y[n] = \sqrt[p]{{\mid x_{i} - x_{j} \mid}^p}\]

where \(x_{i}, x_{j}\) are two different row vectors in the input.

Parameters

  • input (Tensor) – Input tensor of shape \((*B, N, M)\). \(*B\) is batch size, one-dim or multi-dim. dtype: float16, float32 or float64.

  • p (float) – The order of norm distance, \(p∈[0, ∞)\). Default: 2.0 .

Returns

Tensor, has the same dtype as input.

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If dtype of input is not float16, float32 or float64.

  • TypeError – If p is not a float.

  • ValueError – If p is a negative float.

  • ValueError – If dimension of input is less than 2.

Supported Platforms:

GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]).astype(np.float32))
>>> y = ops.pdist(x, p=2.0)
>>> print(y)
[1.4142135 2.828427 1.4142135]